Wydział Elektrotechniki, Elektroniki, Informatyki i Automatyki / Faculty of Electrical, Electronic, Computer and Control Engineering / W2

Stały URI zbioruhttp://hdl.handle.net/11652/2

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20231

Ustawienia

Autorsearch.filters.applied.operator.equals: search.filters.author.Grudzień, KrzysztofTematsearch.filters.applied.operator.equals: search.filters.subject.artificial intelligence

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  • Pozycja
    Performance Analysis of Machine Learning Platforms Using Cloud Native Technology on Edge Devices
    (Wydawnictwo Politechniki Łódzkiej, 2023) Cłapa, Konrad; Grudzień, Krzysztof; Sierszeń, Artur
    This article presents the results of an experiment performed on a machine learning edge computing platform composed of a virtualized environment with a K3s cluster and Kubeflow software. The study aimed to analyze the effectiveness of executing Kubeflow pipelines for simulated parallel executions. A benchmarking environment was developed for the experiment to allow system performance measurements based on parameters, including the number of pipelines and nodes. The results demonstrate the impact of the number of cluster nodes on computational time, revealing insights that could inform future decisions regarding increasing the effectiveness of running machine learning pipelines on edge devices.